Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method, comprising: capturing, by a camera, an image representing a portion of an item, wherein capturing further includes capturing within the portion an identifier for the item along with other areas that surround the identifier and that are part of packaging for the item but are not part of the identifier, the other areas excluding the identifier, wherein the portion comprises the identifier and the other areas; obtaining, by executable instructions that execute on a processor from a non-transitory computer-readable storage medium, the identifier for the item; acquiring, by the executable instructions, a signature corresponding to the portion of the item that is associated with the other areas; calculating, by the executable instructions, an image signature for the image; comparing, by the executable instructions, the signature against the image signature; and issuing, by the executable instructions, an alert to an alert system based on the comparing.
This invention relates to a method for verifying the authenticity of packaged items using image analysis. The problem addressed is ensuring that an item's packaging has not been tampered with or counterfeited, which is critical in industries like pharmaceuticals, luxury goods, and food safety. The method involves capturing an image of a portion of an item's packaging using a camera. The captured image includes both an identifier (such as a barcode, QR code, or serial number) and surrounding packaging areas that are not part of the identifier. The identifier is extracted from the image, and a signature corresponding to the non-identifier areas of the packaging is acquired. This signature represents unique visual characteristics of the packaging, such as texture, patterns, or other distinguishing features. The system then calculates an image signature for the captured image and compares it against the pre-acquired signature. If discrepancies are detected, an alert is issued to an alert system, indicating potential tampering or counterfeiting. This method enhances security by verifying both the identifier and the surrounding packaging, reducing the risk of fraudulent items entering the supply chain.
2. The method of claim 1 , wherein obtaining further includes determining the item identifier based on the image.
A system and method for identifying items using image analysis. The technology addresses the challenge of accurately recognizing and cataloging physical items in various environments, such as retail, logistics, or inventory management, where manual identification is time-consuming and error-prone. The method involves capturing an image of an item and processing the image to extract visual features, such as shapes, colors, or patterns, to determine a unique item identifier. This identifier is then used to retrieve relevant information about the item from a database, enabling automated tracking, classification, or retrieval. The method may also include preprocessing the image to enhance clarity, such as adjusting brightness or removing noise, and applying machine learning models trained on labeled datasets to improve recognition accuracy. The system can be integrated into mobile devices, fixed cameras, or robotic systems for real-time identification in dynamic environments. The approach reduces reliance on manual input, minimizes errors, and improves efficiency in item management workflows.
3. The method of claim 2 , wherein determining further includes resolving the item identifier from a barcode represented within the image.
A system and method for identifying items in an image using barcode recognition. The technology addresses the challenge of accurately detecting and classifying objects in visual data, particularly when traditional object recognition techniques struggle with occlusions, low resolution, or complex backgrounds. The method involves capturing an image containing one or more items, where at least one item includes a barcode. The system processes the image to detect and decode the barcode, extracting an item identifier from the barcode data. This identifier is then used to retrieve additional information about the item, such as its name, category, or other attributes, from a database or external source. The method may also include additional steps such as preprocessing the image to enhance barcode visibility, applying error correction to the decoded barcode data, or validating the extracted identifier against known product databases. The system may further integrate with inventory management, retail checkout, or logistics applications to automate item tracking and identification. The use of barcode recognition improves accuracy and reliability compared to purely visual object recognition, especially in environments where barcodes are widely available.
4. The method of claim 3 , wherein acquiring further includes obtaining the signature as a model signature for an area on the item associated with known packaging features of that item.
The invention relates to a method for acquiring and processing signatures of items, particularly for identifying and verifying packaging features. The method involves obtaining a signature, such as an image or data pattern, from a specific area of an item that corresponds to known packaging features. These features may include labels, barcodes, logos, or other distinctive markings that help identify the item. The signature is captured using sensors or imaging devices and is used to verify the authenticity or integrity of the item. The method may also involve comparing the obtained signature with a reference signature to detect discrepancies, such as tampering or counterfeiting. The system may include a database of reference signatures for different items, allowing for automated verification. The method is particularly useful in supply chain management, quality control, and anti-counterfeiting applications, where accurate identification of packaging features is critical. The invention improves upon existing methods by focusing on specific areas of an item to enhance accuracy and reliability in signature acquisition.
5. The method of claim 1 , wherein acquiring further includes obtaining the signature from a plurality of signatures associated with known packaging features of that item.
A method for identifying and authenticating items based on their packaging features involves capturing an image of the item and extracting a signature from the image. The signature represents unique characteristics of the packaging, such as surface textures, patterns, or other distinguishing features. The method further includes obtaining the signature from a plurality of signatures associated with known packaging features of that item. This allows for comparison against a database of reference signatures to verify authenticity or detect counterfeits. The method may also involve analyzing the signature to detect tampering or inconsistencies in the packaging. The system may use machine learning or pattern recognition techniques to improve accuracy over time. The method is particularly useful in supply chain management, quality control, and anti-counterfeiting applications.
6. The method of claim 5 , wherein obtaining further includes selecting the signature from the plurality of signatures based on an area associated with the portion of the item represented in the image.
This invention relates to image processing and signature selection for identifying or authenticating items, such as documents or products, based on visual features. The problem addressed is the need to accurately select a signature (a unique visual or feature-based representation) from multiple possible signatures for a given item, particularly when the item is partially visible or captured in an image that only shows a portion of it. The solution involves selecting the most appropriate signature based on the area of the item represented in the image, ensuring reliable matching or authentication even when the full item is not visible. The method involves capturing an image of an item, where the image may only show a portion of the item. A plurality of signatures, each representing different areas or aspects of the item, are stored in a database. The method determines the area of the item that is visible in the image and selects the most relevant signature from the database based on this area. This ensures that the selected signature corresponds to the visible portion of the item, improving accuracy in subsequent matching or authentication processes. The selection process may involve comparing the visible area to predefined regions associated with each signature or using spatial mapping to identify the best match. This approach is particularly useful in applications like document verification, product authentication, or quality control, where partial visibility of the item is common.
7. The method of claim 6 , wherein calculating further includes calculating the image signature based on selective points within the image that are associated with the other areas.
The invention relates to image processing, specifically to methods for analyzing images to identify and compare regions of interest. The problem addressed is the need for efficient and accurate image signature calculation, particularly when focusing on specific areas within an image that are relevant to the analysis. Traditional methods may process entire images or arbitrary points, leading to inefficiencies or inaccuracies in identifying meaningful patterns or features. The method involves calculating an image signature, which is a compact representation of the image's key characteristics, by selectively analyzing points within the image that correspond to predefined areas of interest. These areas are determined based on their relevance to the analysis, such as regions containing distinct features or objects. By focusing on these selective points, the method improves computational efficiency and accuracy compared to processing the entire image or random points. The signature calculation may involve extracting features from these points, such as color, texture, or shape, and combining them into a signature that can be used for comparison or classification tasks. This approach is particularly useful in applications like object recognition, image retrieval, or quality control, where identifying specific regions is critical. The method ensures that the signature accurately reflects the most relevant parts of the image, enhancing the reliability of subsequent analyses.
8. The method of claim 1 , wherein calculating includes calculating the image signature based on selective points identified within the image that are associated with the other areas.
This invention relates to image processing, specifically a method for calculating an image signature based on selective points within an image that are associated with other areas of the image. The technology addresses the challenge of efficiently and accurately identifying and characterizing key features in an image for applications such as object recognition, image matching, or content-based retrieval. The method involves analyzing an image to identify specific points that are relevant to other areas within the same image. These points are used to compute an image signature, which serves as a compact representation of the image's distinctive features. By focusing on selective points rather than the entire image, the method improves computational efficiency while maintaining accuracy in feature extraction. The selective points may be determined based on their spatial relationships, intensity values, or other image characteristics that correlate with the areas they are associated with. The image signature derived from these points can be used for various purposes, such as comparing images, detecting similarities, or identifying objects within an image. The method ensures that the signature captures essential information while minimizing redundancy, making it suitable for real-time applications where processing speed is critical. The approach is particularly useful in scenarios where images contain complex backgrounds or multiple objects, as it prioritizes the most relevant points for signature calculation.
9. The method of claim 8 , wherein calculating further includes selecting the selective points as points within the portion of the image that surround and are outside of a barcode represented within the image.
The invention relates to image processing techniques for barcode scanning, specifically improving the accuracy of barcode detection by analyzing surrounding image regions. The problem addressed is the difficulty in accurately identifying and decoding barcodes in images where the barcode is partially obscured, distorted, or surrounded by complex backgrounds. Traditional methods may fail to reliably extract barcode data under such conditions. The method involves selecting specific points within an image that are located around and outside the barcode region. These points are used to calculate additional data that helps refine the barcode detection process. By analyzing the surrounding areas, the system can better distinguish the barcode from interfering elements, improving recognition accuracy. The selected points may be chosen based on their spatial relationship to the barcode, ensuring they provide relevant contextual information without being part of the barcode itself. This approach enhances the robustness of barcode scanning in real-world scenarios where images often contain noise, shadows, or other visual clutter. The technique is particularly useful in applications like mobile scanning, automated inventory systems, and document processing where reliable barcode extraction is critical.
10. A method, comprising: determining, by executable instructions that execute on a processor from a non-transitory computer-readable storage medium, an item identifier for an item during a transaction from a first portion of an item image captured for the item; calculating, by the executable instructions, a signature for a second portion of the item image, wherein the second portion are other areas of the item that do not include the first portion, wherein the second portion comprising packaging features for the item, wherein the second portion excludes the first portion; and raising, by the executable instructions, an alert when the signature fails to match a model signature associated with the second portion of the item image.
This invention relates to automated item verification during transactions, specifically detecting tampering or counterfeit items by analyzing packaging features. The method involves capturing an image of an item and processing it to identify the item and verify its packaging integrity. A first portion of the image is used to determine an item identifier, such as a barcode or label, while a second portion, comprising packaging features like seals, holograms, or other security elements, is analyzed separately. A signature is calculated for the second portion, which excludes the first portion, and compared against a pre-stored model signature for that item. If the calculated signature does not match the model, an alert is raised, indicating potential tampering or counterfeit packaging. This approach ensures that both the item itself and its packaging are authenticated, enhancing security in transaction processing. The method is implemented via executable instructions on a processor, ensuring real-time verification during transactions. The system is particularly useful in retail, logistics, and security applications where item authenticity is critical.
11. The method of claim 10 , wherein calculating further includes identifying the second portion as an area within the item image that surrounds the first portion.
This invention relates to image processing techniques for analyzing items, particularly for identifying and distinguishing regions within an item image. The problem addressed is the need to accurately segment and analyze different portions of an item image, such as distinguishing between a primary region of interest and surrounding areas, to improve subsequent processing or decision-making steps. The method involves calculating a relationship between two portions of an item image. The first portion is identified as a specific area of interest within the image, such as a defect, feature, or boundary. The second portion is determined to be an area that surrounds the first portion, effectively creating a boundary or buffer region around the primary area of interest. This surrounding area may be defined by a fixed distance, a dynamic threshold, or other criteria to ensure it encompasses relevant contextual information while excluding irrelevant regions. By identifying the second portion as the surrounding area, the method enables more precise analysis of the item image. This can be used for tasks such as defect detection, quality control, or feature extraction, where distinguishing between the primary region and its surroundings is critical. The technique ensures that the surrounding area is properly segmented, allowing for accurate measurements, comparisons, or further processing steps to be performed on the identified regions. The method improves the reliability and accuracy of image-based analysis systems by providing a clear distinction between the primary area of interest and its surrounding context.
12. The method of claim 10 , wherein calculating further includes selecting the second portion of the item image based on model signature and an area on the item from which the model signature is based.
The invention relates to image processing techniques for analyzing items, particularly in automated systems where visual inspection or identification is required. The problem addressed is the need for accurate and efficient extraction of relevant features from item images, especially when dealing with complex or varied backgrounds or when only specific portions of the image contain meaningful data. The method involves processing an image of an item to identify and extract a second portion of the image, distinct from a first portion, for further analysis. The selection of the second portion is based on a model signature—a predefined pattern or feature derived from a specific area of the item. This model signature serves as a reference to guide the extraction process, ensuring that the selected portion corresponds to the relevant area of the item. The method improves accuracy by focusing on the most informative regions of the image, reducing noise and irrelevant data from the analysis. This approach is particularly useful in applications such as quality control, object recognition, or automated sorting, where precise feature extraction is critical. The technique ensures that the extracted portion aligns with the area from which the model signature was originally derived, maintaining consistency and reliability in the analysis.
13. The method of claim 12 , wherein selecting further includes selecting the second portion as positions within the image that do not include an item barcode, wherein the item barcode present within the first portion of the item image.
This invention relates to image processing for barcode scanning, specifically addressing challenges in accurately capturing and processing barcodes in images where the barcode is partially obscured or surrounded by other elements. The method involves analyzing an item image to identify and isolate a barcode region while excluding non-barcode areas to improve scanning accuracy. The process begins by capturing an image containing an item with a barcode. The image is then divided into at least two portions: a first portion containing the barcode and a second portion that does not include the barcode. The second portion is selected based on regions of the image that lack barcode data, ensuring that only the relevant barcode area is processed. This selective isolation helps reduce interference from surrounding elements, such as text, logos, or other visual noise, which can degrade barcode recognition performance. The method enhances barcode scanning reliability in real-world scenarios where items may be partially obscured or contain complex backgrounds. By focusing processing efforts on the barcode-containing region, the system improves accuracy and efficiency in automated scanning applications.
14. The method of claim 10 , wherein alerting further includes halting transaction processing associated with the transaction.
A system and method for detecting and responding to fraudulent transactions in financial systems. The invention addresses the problem of real-time fraud detection by analyzing transaction data to identify suspicious patterns or anomalies that may indicate fraudulent activity. When a potentially fraudulent transaction is detected, the system generates an alert to notify relevant parties, such as financial institutions or users, and takes immediate action to mitigate the risk. This includes halting the processing of the transaction to prevent unauthorized financial losses. The method involves monitoring transaction data in real-time, applying predefined fraud detection rules or machine learning models to assess risk, and triggering automated responses when fraud is suspected. The system may also log transaction details for further investigation and compliance purposes. By integrating fraud detection with transaction processing, the invention provides a proactive approach to minimizing financial fraud and enhancing security in digital transactions.
15. The method of claim 14 , wherein halting further includes providing a video representing a time frame when the item image was captured for the transaction.
A system and method for transaction monitoring and fraud detection in retail environments involves capturing images of items during transactions and analyzing them to detect discrepancies. The system uses image processing to compare captured item images with reference images stored in a database, identifying mismatches that may indicate fraud or errors. When a discrepancy is detected, the system halts the transaction and provides additional verification steps. In one implementation, the system enhances fraud detection by displaying a video segment showing the exact time frame when the item image was captured. This video allows staff or automated systems to review the context of the capture, such as the item's placement, handling, or environmental conditions, to further validate the transaction or identify potential tampering. The video may include timestamps and other metadata to ensure accuracy and traceability. This approach improves fraud detection by providing visual evidence that complements the image analysis, reducing false positives and enhancing the reliability of transaction verification. The system is particularly useful in high-volume retail settings where manual inspection is impractical.
16. The method of claim 15 , wherein providing further includes providing other item images captured at different angles for the item within the time frame.
This invention relates to a system for capturing and analyzing images of items, particularly for use in inventory management, quality control, or authentication processes. The core problem addressed is the need for comprehensive visual documentation of items from multiple perspectives to ensure accurate identification, verification, or assessment. Traditional single-angle imaging often fails to capture sufficient detail, leading to errors in item recognition or quality assessment. The method involves capturing multiple images of an item from different angles within a specified time frame. These images are then processed to generate a detailed visual record, which can be used for comparison, analysis, or verification purposes. The system may include a camera or imaging device capable of rotating around the item or capturing images from various fixed positions to ensure full coverage. The captured images are synchronized and stored in a database for later retrieval and analysis. This approach enhances the accuracy of item identification, reduces errors in quality control, and improves authentication processes by providing a more complete visual representation of the item. The method is particularly useful in industries where precise visual documentation is critical, such as manufacturing, logistics, or security.
17. The method of claim 15 further comprising, sending, by the executable instructions, the video to a remote agent for review of the video while the transaction remains halted.
This invention relates to a system for monitoring and managing transactions involving video content, particularly in scenarios where automated analysis may require human intervention. The system detects potential issues during a transaction, such as suspicious activity or content violations, and automatically halts the transaction to prevent further processing. Upon halting, the system sends the video to a remote agent for manual review while the transaction remains paused. The remote agent can then assess the video, determine whether the transaction should proceed, and take appropriate action. This ensures that transactions are only completed after proper verification, reducing risks such as fraud, policy violations, or unauthorized content distribution. The system integrates automated detection with human oversight, improving accuracy and compliance in transaction processing. The method involves real-time monitoring, automated halting, and remote agent intervention, providing a robust solution for high-stakes video transactions.
18. The method of claim 10 , wherein calculating further includes correcting image distortions present within the second portion of the item image before calculating the signature.
This invention relates to image processing techniques for analyzing items, particularly for identifying and correcting distortions in captured images to improve accuracy in subsequent analysis. The method involves capturing an image of an item, where the image includes a first portion and a second portion. The first portion is processed to generate a signature, which is a unique representation of the item's features. The second portion, which may contain distortions such as perspective, lens, or motion blur, is corrected before calculating its signature. Distortion correction may involve geometric transformations, filtering, or other image enhancement techniques to align or sharpen the second portion. The corrected second portion is then used to calculate its signature, ensuring that the final analysis is based on accurate, distortion-free data. This method is useful in applications like object recognition, quality control, or authentication, where precise image analysis is critical. The invention improves reliability by ensuring that distortions do not affect the signature calculation, leading to more consistent and accurate results.
19. A device, comprising: a processor; a non-transitory computer-readable storage medium having executable instructions; and the executable instructions when executed by the processor from the non-transitory computer-readable storage medium cause the processor to: identify an item from a first portion of an item image captured of the item; obtain model signatures for the item; select a second portion of the item image based on areas associated with the item from which the model signatures are based, wherein the second portion excludes the first portion, the second portion surrounding the first portion, and the second portion is associated with other areas of the item that do not include the first portion, wherein the second portion comprising packaging features for the item; calculate an item signature based on at least one point represented for the item in the second portion of the item image; identify a particular model signature based on the at least one point; compare the particular model signature against the item signature that was calculated; and raise at least one alert when the particular model signature does not match the item signature.
This invention relates to a device for verifying the authenticity of items, particularly focusing on detecting counterfeit or tampered products by analyzing their packaging features. The device addresses the challenge of ensuring product integrity by comparing visual signatures of items against known model signatures to identify discrepancies. The device includes a processor and a non-transitory computer-readable storage medium containing executable instructions. When executed, the instructions cause the processor to identify an item within a captured image, then obtain pre-existing model signatures for that item. The device selects a second portion of the image surrounding the initially identified item, excluding the first portion, to focus on packaging features. It calculates an item signature based on points within this second portion and compares it against the model signature. If the signatures do not match, the device raises an alert, indicating potential tampering or counterfeiting. The system leverages image analysis to detect inconsistencies in packaging, ensuring that the item's visual characteristics align with expected model data. This approach enhances security by focusing on areas beyond the primary item, such as packaging, which may contain subtle but critical indicators of authenticity. The device automates the verification process, reducing human error and improving efficiency in quality control and anti-counterfeiting efforts.
20. The device of claim 19 , wherein the first portion is an item barcode area representing an item barcode on the item.
A system for item identification and tracking in retail or inventory management environments addresses the challenge of efficiently capturing and processing item data. The system includes a device with a first portion that functions as an item barcode area, representing a barcode on the item being scanned or tracked. This barcode area is designed to capture or display the barcode data, enabling accurate identification of the item. The device may also include a second portion that interacts with the first portion to facilitate data processing, such as decoding the barcode or transmitting the information to a database. The system ensures reliable item recognition by aligning the barcode area with the item's barcode, reducing errors in scanning or tracking. This solution improves efficiency in inventory management, point-of-sale systems, and automated retail applications by streamlining the barcode reading process. The device may be integrated into handheld scanners, fixed scanners, or mobile devices, depending on the application. The barcode area is optimized for compatibility with various barcode formats, ensuring broad applicability across different retail and logistics environments.
Unknown
November 3, 2020
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